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Area of Science:

  • Computational neuroscience
  • Complex systems analysis

Background:

  • Whole-brain models integrate connectivity with local neural dynamics.
  • Nonlinear oscillators (Hopf bifurcation) link brain connectivity to collective dynamics.

Purpose of the Study:

  • Analyze linear fluctuations in whole-brain models.
  • Estimate stationary statistics like covariances and power spectral densities.

Main Methods:

  • Linear approximation of nonlinear oscillator dynamics.
  • Analytical estimation of statistical properties.
  • Fast parameter exploration for model analysis.

Main Results:

  • Accurate estimation of instantaneous and lagged covariances.
  • Accurate estimation of power spectral densities.
  • Validation of linear approximation with heterogeneous parameters and time-delays.

Conclusions:

  • Linear fluctuation analysis provides efficient tools for whole-brain model studies.
  • Enables rapid exploration of brain state changes and connectivity alterations.
  • Facilitates understanding of parameter modulations in non-equilibrium dynamics.